Related papers: foREST: A Tree-based Approach for Fuzzing RESTful …
Deep learning is being used extensively in a variety of software engineering tasks, e.g., program classification and defect prediction. Although the technique eliminates the required process of feature engineering, the construction of…
We present AuthREST, an open-source security testing tool targeting broken authentication, one of the most prevalent API security risks in the wild. AuthREST automatically tests web APIs for credential stuffing, password brute forcing, and…
Large Language Models (LLMs) face significant inference latency challenges stemming from their autoregressive design and large size. To address this, speculative decoding emerges as a solution, enabling the simultaneous generation and…
Most of the web services are offered in the form of RESTful APIs. This has led to an active research interest in API testing to ensure the reliability of these services. While most of the testing techniques proposed in the past rely on the…
Incremental learning aims to adapt to new sets of categories over time with minimal computational overhead. Prior work often addresses this task by training efficient task-specific adaptors that modify frozen layer weights or features to…
Since only a small number of traces generated from distributed tracing helps in troubleshooting, its storage requirement can be significantly reduced by biasing the selection towards anomalous traces. To aid in this scenario, we propose…
Referring image segmentation is an advanced semantic segmentation task where target is not a predefined class but is described in natural language. Most of existing methods for this task rely heavily on convolutional neural networks, which…
The fast-growing Web API landscape brings clients more options than ever before---in theory. In practice, they cannot easily switch between different providers offering similar functionality. We discuss a vision for developing Web APIs…
Heuristic-based methods are among the most popular methods in the process discovery area. This category of methods is composed of two main steps: 1) discovering a dependency graph 2) determining the split/join patterns of the dependency…
Logs record valuable system information at runtime. They are widely used by data-driven approaches for development and monitoring purposes. Parsing log messages to structure their format is a classic preliminary step for log-mining tasks.…
We present graph-based translation models which translate source graphs into target strings. Source graphs are constructed from dependency trees with extra links so that non-syntactic phrases are connected. Inspired by phrase-based models,…
Due to the dynamic nature of real-world graphs, there has been a growing interest in the graph-streaming setting where a continuous stream of graph updates is mixed with arbitrary graph queries. In principle, purely-functional trees are an…
Service fingerprinting (i.e. the identification of network services and other applications on computing systems) is an essential part of penetration tests. The main contribution of this paper is a study on the improvement of fingerprinting…
We propose Range-based Ternary Search Tree (RTST), a tree-based approach for flow-table lookup in SDN network. RTST builds upon flow-tables in SDN switches to provide a fast lookup among flows. We present a parallel multi-pipeline…
We propose a technique for learning representations of parser states in transition-based dependency parsers. Our primary innovation is a new control structure for sequence-to-sequence neural networks---the stack LSTM. Like the conventional…
Network-facing applications are commonly exposed to all kinds of attacks, especially when connected to the internet. As a result, web servers like Nginx or client applications such as curl make every effort to secure and harden their code…
Modern cloud services are prone to failures due to their complex architecture, making diagnosis a critical process. Site Reliability Engineers (SREs) spend hours leveraging multiple sources of data, including the alerts, error logs, and…
Continual learning based on data stream mining deals with ubiquitous sources of Big Data arriving at high-velocity and in real-time. Adaptive Random Forest ({\em ARF}) is a popular ensemble method used for continual learning due to its…
In recent years, fuzz testing has benefited from increased computational power and important algorithmic advances, leading to systems that have discovered many critical bugs and vulnerabilities in production software. Despite these…
Adapting a large language model for multiple-attribute text style transfer via fine-tuning can be challenging due to the significant amount of computational resources and labeled data required for the specific task. In this paper, we…